Predictive determination

ABSTRACT

Systems, methods and computer readable media are disclosed for a gesture recognizer system architecture. A recognizer engine is provided, which receives user motion data and provides that data to a plurality of filters. A filter corresponds to a gesture, that may then be tuned by an application receiving information from the gesture recognizer so that the specific parameters of the gesture—such as an arm acceleration for a throwing gesture—may be set on a per-application level, or multiple times within a single application. Each filter may output to an application using it a confidence level that the corresponding gesture occurred, as well as further details about the user motion data.

PRIORITY

The present application is a continuation of application Ser. No.12/422,769, titled “Predictive Determination,” filed Apr. 13, 2009,which is a continuation-in-part of application Ser. No. 12/422,661,titled “Gesture Recognizer System Architecture,” filed Apr. 13, 2009,which in turn claims priority to provisional application 61/148,866,titled “Gesture Recognizer System Architecture,” filed Jan. 30, 2009,the contents of which are incorporated herein in their entirety.

BACKGROUND OF THE INVENTION

Many computing applications such as computer games, multimediaapplications, office applications or the like use controls to allowusers to manipulate game characters or other aspects of an application.Typically such controls are input using, for example, controllers,remotes, keyboards, mice, or the like. Unfortunately, such controls canbe difficult to learn, thus creating a barrier between a user and suchgames and applications. Furthermore, such controls may be different thanactual game actions or other application actions for which the controlsare used. For example, a game control that causes a game character toswing a baseball bat may not correspond to an actual motion of swingingthe baseball bat.

SUMMARY OF THE INVENTION

Disclosed herein are systems and methods for receiving data reflectingskeletal movement of a user, and determining from that data whether theuser has performed one or more gestures. A gesture recognizer systemarchitecture is disclosed from which application developers canincorporate gesture recognition into their applications.

In an embodiment, a recognizer engine comprises a base recognizer engineand at least one filter. A filter comprises information about a gestureand may comprise at least one corresponding parameter. The recognizerengine provides a filter to an application and receives from thatapplication at least one parameter that specifies the particulars of howthat gesture is to be recognized by the recognizer engine.

The recognizer engine receives a series of image data from a camera.This camera may comprise a color camera (such as red-green-blue or RGB),a depth camera, and a three-dimensional (3D) camera. This data maycomprise separate depth and color images, a combined image thatincorporates depth and color information, or a parsed image whereobjects are identified, such as people that are skeletal mapped. Thisdata captures motions or poses made by at least one user. Based on thisimage data, the recognizer engine is able to parse gestures that theuser intends to convey. The recognizer engine detects the likelihoodthat the user has conveyed a gesture, and that the user has satisfiedany parameters, either default or application-determined, associatedwith the gesture for the application. The recognizer engine then sendsthe confidence level that this has occurred to the application. Insending this confidence level, the recognizer engine may also send theapplication specifics of how the user conveyed the gesture for furtherprocessing by the application.

The foregoing is a summary and thus contains, by necessity,simplifications, generalizations and omissions of detail. Those skilledin the art will appreciate that the summary is illustrative only and isnot intended to be in any way limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The systems, methods, and computer readable media for a gesturerecognizer system architecture in accordance with this specification arefurther described with reference to the accompanying drawings in which:

FIGS. 1A and 1B illustrate an example embodiment of a targetrecognition, analysis, and tracking system with a user playing a game.

FIG. 2 illustrates an example embodiment of a capture device that may beused in a target recognition, analysis, and tracking system.

FIG. 3A illustrates an example embodiment of a computing environmentthat may be used to interpret one or more gestures in a targetrecognition, analysis, and tracking system.

FIG. 3B illustrates another example embodiment of a computingenvironment that may be used to interpret one or more gestures in atarget recognition, analysis, and tracking system.

FIG. 4A illustrates a skeletal mapping of a user that has been generatedfrom the target recognition, analysis, and tracking system of FIG. 2.

FIG. 4B illustrates further details of the gesture recognizerarchitecture shown in FIG. 2.

FIGS. 5A and 5B illustrate how gesture filters may be stacked to createmore complex gesture filters.

FIGS. 6A, 6B, 6C, 6D, and 6E illustrate an example gesture that a usermay make to signal for a “fair catch” in football video game.

FIGS. 7A, 7B, 7C, 7D, and 7E illustrate the example “fair catch” gestureof FIGS. 6A, 6B, 6C, 6D, and 6E as each frame of image data has beenparsed to produce a skeletal map of the user.

FIG. 8 illustrates exemplary operational procedures for using a gesturerecognizer architecture to provide gestures to at least one application.

FIG. 9A illustrates a graph of filter output with non-predictive gesturerecognition.

FIG. 9B illustrates a graph of filter output with predictive gesturerecognition.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

As will be described herein, a user may control an application executingon a computing environment such as a game console, a computer, or thelike by performing one or more gestures. According to one embodiment,the gestures may be received by, for example, a capture device. Forexample, the capture device may capture a depth image of a scene. In oneembodiment, the capture device may determine whether one or more targetsor objects in the scene corresponds to a human target such as the user.To determine whether a target or object in the scene corresponds a humantarget, each of the targets may be flood filled and compared to apattern of a human body model. Each target or object that matches thehuman body model may then be scanned to generate a skeletal modelassociated therewith. The skeletal model may then be provided to thecomputing environment such that the computing environment may track theskeletal model, render an avatar associated with the skeletal model, andmay determine which controls to perform in an application executing onthe computer environment based on, for example, gestures of the userthat have been recognized from the skeletal model. A gesture recognizerengine, the architecture of which is described more fully below, is usedto determine when a particular gesture has been made by the user.

FIGS. 1A and 1B illustrate an example embodiment of a configuration of atarget recognition, analysis, and tracking system 10 with a user 18playing a boxing game. In an example embodiment, the target recognition,analysis, and tracking system 10 may be used to recognize, analyze,and/or track a human target such as the user 18.

As shown in FIG. 1A, the target recognition, analysis, and trackingsystem 10 may include a computing environment 12. The computingenvironment 12 may be a computer, a gaming system or console, or thelike. According to an example embodiment, the computing environment 12may include hardware components and/or software components such that thecomputing environment 12 may be used to execute applications such asgaming applications, non-gaming applications, or the like.

As shown in FIG. 1A, the target recognition, analysis, and trackingsystem 10 may further include a capture device 20. The capture device 20may be, for example, a camera that may be used to visually monitor oneor more users, such as the user 18, such that gestures performed by theone or more users may be captured, analyzed, and tracked to perform oneor more controls or actions within an application, as will be describedin more detail below.

According to one embodiment, the target recognition, analysis, andtracking system 10 may be connected to an audiovisual device 16 such asa television, a monitor, a high-definition television (HDTV), or thelike that may provide game or application visuals and/or audio to a usersuch as the user 18. For example, the computing environment 12 mayinclude a video adapter such as a graphics card and/or an audio adaptersuch as a sound card that may provide audiovisual signals associatedwith the game application, non-game application, or the like. Theaudiovisual device 16 may receive the audiovisual signals from thecomputing environment 12 and may then output the game or applicationvisuals and/or audio associated with the audiovisual signals to the user18. According to one embodiment, the audiovisual device 16 may beconnected to the computing environment 12 via, for example, an S-Videocable, a coaxial cable, an HDMI cable, a DVI cable, a VGA cable, or thelike.

As shown in FIGS. 1A and 1B, the target recognition, analysis, andtracking system 10 may be used to recognize, analyze, and/or track ahuman target such as the user 18. For example, the user 18 may betracked using the capture device 20 such that the movements of user 18may be interpreted as controls that may be used to affect theapplication being executed by computer environment 12. Thus, accordingto one embodiment, the user 18 may move his or her body to control theapplication.

As shown in FIGS. 1A and 1B, in an example embodiment, the applicationexecuting on the computing environment 12 may be a boxing game that theuser 18 may be playing. For example, the computing environment 12 mayuse the audiovisual device 16 to provide a visual representation of aboxing opponent 22 to the user 18. The computing environment 12 may alsouse the audiovisual device 16 to provide a visual representation of aplayer avatar 24 that the user 18 may control with his or her movements.For example, as shown in FIG. 1B, the user 18 may throw a punch inphysical space to cause the player avatar 24 to throw a punch in gamespace. Thus, according to an example embodiment, the computerenvironment 12 and the capture device 20 of the target recognition,analysis, and tracking system 10 may be used to recognize and analyzethe punch of the user 18 in physical space such that the punch may beinterpreted as a game control of the player avatar 24 in game space.

Other movements by the user 18 may also be interpreted as other controlsor actions, such as controls to bob, weave, shuffle, block, jab, orthrow a variety of different power punches. Furthermore, some movementsmay be interpreted as controls that may correspond to actions other thancontrolling the player avatar 24. For example, the player may usemovements to end, pause, or save a game, select a level, view highscores, communicate with a friend, etc.

In example embodiments, the human target such as the user 18 may have anobject. In such embodiments, the user of an electronic game may beholding the object such that the motions of the player and the objectmay be used to adjust and/or control parameters of the game. Forexample, the motion of a player holding a racket may be tracked andutilized for controlling an on-screen racket in an electronic sportsgame. In another example embodiment, the motion of a player holding anobject may be tracked and utilized for controlling an on-screen weaponin an electronic combat game.

According to other example embodiments, the target recognition,analysis, and tracking system 10 may further be used to interpret targetmovements as operating system and/or application controls that areoutside the realm of games. For example, virtually any controllableaspect of an operating system and/or application may be controlled bymovements of the target such as the user 18.

FIG. 2 illustrates an example embodiment of the capture device 20 thatmay be used in the target recognition, analysis, and tracking system 10.According to an example embodiment, the capture device 20 may beconfigured to capture video with depth information including a depthimage that may include depth values via any suitable techniqueincluding, for example, time-of-flight, structured light, stereo image,or the like. According to one embodiment, the capture device 20 mayorganize the calculated depth information into “Z layers,” or layersthat may be perpendicular to a Z axis extending from the depth cameraalong its line of sight.

As shown in FIG. 2, the capture device 20 may include an image cameracomponent 22. According to an example embodiment, the image cameracomponent 22 may be a depth camera that may capture the depth image of ascene. The depth image may include a two-dimensional (2-D) pixel area ofthe captured scene where each pixel in the 2-D pixel area may representa length in, for example, centimeters, millimeters, or the like of anobject in the captured scene from the camera.

As shown in FIG. 2, according to an example embodiment, the image cameracomponent 22 may include an IR light component 24, a three-dimensional(3-D) camera 26, and an RGB camera 28 that may be used to capture thedepth image of a scene. For example, in time-of-flight analysis, the IRlight component 24 of the capture device 20 may emit an infrared lightonto the scene and may then use sensors (not shown) to detect thebackscattered light from the surface of one or more targets and objectsin the scene using, for example, the 3-D camera 26 and/or the RGB camera28. In some embodiments, pulsed infrared light may be used such that thetime between an outgoing light pulse and a corresponding incoming lightpulse may be measured and used to determine a physical distance from thecapture device 20 to a particular location on the targets or objects inthe scene. Additionally, in other example embodiments, the phase of theoutgoing light wave may be compared to the phase of the incoming lightwave to determine a phase shift. The phase shift may then be used todetermine a physical distance from the capture device to a particularlocation on the targets or objects.

According to another example embodiment, time-of-flight analysis may beused to indirectly determine a physical distance from the capture device20 to a particular location on the targets or objects by analyzing theintensity of the reflected beam of light over time via varioustechniques including, for example, shuttered light pulse imaging.

In another example embodiment, the capture device 20 may use astructured light to capture depth information. In such an analysis,patterned light (i.e., light displayed as a known pattern such as gridpattern or a stripe pattern) may be projected onto the scene via, forexample, the IR light component 24. Upon striking the surface of one ormore targets or objects in the scene, the pattern may become deformed inresponse. Such a deformation of the pattern may be captured by, forexample, the 3-D camera 26 and/or the RGB camera 28 and may then beanalyzed to determine a physical distance from the capture device to aparticular location on the targets or objects.

According to another embodiment, the capture device 20 may include twoor more physically separated cameras that may view a scene fromdifferent angles, to obtain visual stereo data that may be resolved togenerate depth information.

The capture device 20 may further include a microphone 30. Themicrophone 30 may include a transducer or sensor that may receive andconvert sound into an electrical signal. According to one embodiment,the microphone 30 may be used to reduce feedback between the capturedevice 20 and the computing environment 12 in the target recognition,analysis, and tracking system 10. Additionally, the microphone 30 may beused to receive audio signals that may also be provided by the user tocontrol applications such as game applications, non-game applications,or the like that may be executed by the computing environment 12.

In an example embodiment, the capture device 20 may further include aprocessor 32 that may be in operative communication with the imagecamera component 22. The processor 32 may include a standardizedprocessor, a specialized processor, a microprocessor, or the like thatmay execute instructions that may include instructions for receiving thedepth image, determining whether a suitable target may be included inthe depth image, converting the suitable target into a skeletalrepresentation or model of the target, or any other suitableinstruction.

The capture device 20 may further include a memory component 34 that maystore the instructions that may be executed by the processor 32, imagesor frames of images captured by the 3-D camera or RGB camera, or anyother suitable information, images, or the like. According to an exampleembodiment, the memory component 34 may include random access memory(RAM), read only memory (ROM), cache, Flash memory, a hard disk, or anyother suitable storage component. As shown in FIG. 2, in one embodiment,the memory component 34 may be a separate component in communicationwith the image capture component 22 and the processor 32. According toanother embodiment, the memory component 34 may be integrated into theprocessor 32 and/or the image capture component 22.

As shown in FIG. 2, the capture device 20 may be in communication withthe computing environment 12 via a communication link 36. Thecommunication link 36 may be a wired connection including, for example,a USB connection, a Firewire connection, an Ethernet cable connection,or the like and/or a wireless connection such as a wireless 802.11b, g,a, or n connection. According to one embodiment, the computingenvironment 12 may provide a clock to the capture device 20 that may beused to determine when to capture, for example, a scene via thecommunication link 36.

Additionally, the capture device 20 may provide the depth informationand images captured by, for example, the 3-D camera 26 and/or the RGBcamera 28, and a skeletal model that may be generated by the capturedevice 20 to the computing environment 12 via the communication link 36.The computing environment 12 may then use the skeletal model, depthinformation, and captured images to, for example, recognize usergestures and in response control an application such as a game or wordprocessor. For example, as shown, in FIG. 2, the computing environment12 may include a gestures recognizer engine 190. The gestures recognizerengine 190 may include a collection of gesture filters, each comprisinginformation concerning a gesture that may be performed by the skeletalmodel (as the user moves). The data captured by the cameras 26, 28 anddevice 20 in the form of the skeletal model and movements associatedwith it may be compared to the gesture filters in the gesture recognizerengine 190 to identify when a user (as represented by the skeletalmodel) has performed one or more gestures. Those gestures may beassociated with various controls of an application. Thus, the computingenvironment 12 may use the gesture recognizer engine 190 to interpretmovements of the skeletal model and to control an application based onthe movements.

FIG. 3A illustrates an example embodiment of a computing environmentthat may be used to interpret one or more gestures in a targetrecognition, analysis, and tracking system. The computing environmentsuch as the computing environment 12 described above with respect toFIGS. 1A-2 may be a multimedia console 100, such as a gaming console. Asshown in FIG. 3A, the multimedia console 100 has a central processingunit (CPU) 101 having a level 1 cache 102, a level 2 cache 104, and aflash ROM (Read Only Memory) 106. The level 1 cache 102 and a level 2cache 104 temporarily store data and hence reduce the number of memoryaccess cycles, thereby improving processing speed and throughput. TheCPU 101 may be provided having more than one core, and thus, additionallevel 1 and level 2 caches 102 and 104. The flash ROM 106 may storeexecutable code that is loaded during an initial phase of a boot processwhen the multimedia console 100 is powered ON.

A graphics processing unit (GPU) 108 and a video encoder/video codec(coder/decoder) 114 form a video processing pipeline for high speed andhigh resolution graphics processing. Data is carried from the graphicsprocessing unit 108 to the video encoder/video codec 114 via a bus. Thevideo processing pipeline outputs data to an A/V (audio/video) port 140for transmission to a television or other display. A memory controller110 is connected to the GPU 108 to facilitate processor access tovarious types of memory 112, such as, but not limited to, a RAM (RandomAccess Memory).

The multimedia console 100 includes an I/O controller 120, a systemmanagement controller 122, an audio processing unit 123, a networkinterface controller 124, a first USB host controller 126, a second USBcontroller 128 and a front panel I/O subassembly 130 that are preferablyimplemented on a module 118. The USB controllers 126 and 128 serve ashosts for peripheral controllers 142(1)-142(2), a wireless adapter 148,and an external memory device 146 (e.g., flash memory, external CD/DVDROM drive, removable media, etc.). The network interface 124 and/orwireless adapter 148 provide access to a network (e.g., the Internet,home network, etc.) and may be any of a wide variety of various wired orwireless adapter components including an Ethernet card, a modem, aBluetooth module, a cable modem, and the like.

System memory 143 is provided to store application data that is loadedduring the boot process. A media drive 144 is provided and may comprisea DVD/CD drive, hard drive, or other removable media drive, etc. Themedia drive 144 may be internal or external to the multimedia console100. Application data may be accessed via the media drive 144 forexecution, playback, etc. by the multimedia console 100. The media drive144 is connected to the I/O controller 120 via a bus, such as a SerialATA bus or other high speed connection (e.g., IEEE 1394).

The system management controller 122 provides a variety of servicefunctions related to assuring availability of the multimedia console100. The audio processing unit 123 and an audio codec 132 form acorresponding audio processing pipeline with high fidelity and stereoprocessing. Audio data is carried between the audio processing unit 123and the audio codec 132 via a communication link. The audio processingpipeline outputs data to the A/V port 140 for reproduction by anexternal audio player or device having audio capabilities.

The front panel I/O subassembly 130 supports the functionality of thepower button 150 and the eject button 152, as well as any LEDs (lightemitting diodes) or other indicators exposed on the outer surface of themultimedia console 100. A system power supply module 136 provides powerto the components of the multimedia console 100. A fan 138 cools thecircuitry within the multimedia console 100.

The CPU 101, GPU 108, memory controller 110, and various othercomponents within the multimedia console 100 are interconnected via oneor more buses, including serial and parallel buses, a memory bus, aperipheral bus, and a processor or local bus using any of a variety ofbus architectures. By way of example, such architectures can include aPeripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.

When the multimedia console 100 is powered ON, application data may beloaded from the system memory 143 into memory 112 and/or caches 102, 104and executed on the CPU 101. The application may present a graphicaluser interface that provides a consistent user experience whennavigating to different media types available on the multimedia console100. In operation, applications and/or other media contained within themedia drive 144 may be launched or played from the media drive 144 toprovide additional functionalities to the multimedia console 100.

The multimedia console 100 may be operated as a standalone system bysimply connecting the system to a television or other display. In thisstandalone mode, the multimedia console 100 allows one or more users tointeract with the system, watch movies, or listen to music. However,with the integration of broadband connectivity made available throughthe network interface 124 or the wireless adapter 148, the multimediaconsole 100 may further be operated as a participant in a larger networkcommunity.

When the multimedia console 100 is powered ON, a set amount of hardwareresources are reserved for system use by the multimedia consoleoperating system. These resources may include a reservation of memory(e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth(e.g., 8 kb/s), etc. Because these resources are reserved at system boottime, the reserved resources do not exist from the application's view.

In particular, the memory reservation preferably is large enough tocontain the launch kernel, concurrent system applications and drivers.The CPU reservation is preferably constant such that if the reserved CPUusage is not used by the system applications, an idle thread willconsume any unused cycles.

With regard to the GPU reservation, lightweight messages generated bythe system applications (e.g., popups) are displayed by using a GPUinterrupt to schedule code to render popup into an overlay. The amountof memory required for an overlay depends on the overlay area size andthe overlay preferably scales with screen resolution. Where a full userinterface is used by the concurrent system application, it is preferableto use a resolution independent of application resolution. A scaler maybe used to set this resolution such that the need to change frequencyand cause a TV resynch is eliminated.

After the multimedia console 100 boots and system resources arereserved, concurrent system applications execute to provide systemfunctionalities. The system functionalities are encapsulated in a set ofsystem applications that execute within the reserved system resourcesdescribed above. The operating system kernel identifies threads that aresystem application threads versus gaming application threads. The systemapplications are preferably scheduled to run on the CPU 101 atpredetermined times and intervals in order to provide a consistentsystem resource view to the application. The scheduling is to minimizecache disruption for the gaming application running on the console.

When a concurrent system application requires audio, audio processing isscheduled asynchronously to the gaming application due to timesensitivity. A multimedia console application manager (described below)controls the gaming application audio level (e.g., mute, attenuate) whensystem applications are active.

Input devices (e.g., controllers 142(1) and 142(2)) are shared by gamingapplications and system applications. The input devices are not reservedresources, but are to be switched between system applications and thegaming application such that each will have a focus of the device. Theapplication manager preferably controls the switching of input stream,without knowledge the gaming application's knowledge and a drivermaintains state information regarding focus switches. The cameras 26, 28and capture device 20 may define additional input devices for theconsole 100.

FIG. 3B illustrates another example embodiment of a computingenvironment 220 that may be the computing environment 12 shown in FIGS.1A-2 used to interpret one or more gestures in a target recognition,analysis, and tracking system. The computing system environment 220 isonly one example of a suitable computing environment and is not intendedto suggest any limitation as to the scope of use or functionality of thepresently disclosed subject matter. Neither should the computingenvironment 220 be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment 220. In some embodiments the variousdepicted computing elements may include circuitry configured toinstantiate specific aspects of the present disclosure. For example, theterm circuitry used in the disclosure can include specialized hardwarecomponents configured to perform function(s) by firmware or switches. Inother examples embodiments the term circuitry can include a generalpurpose processing unit, memory, etc., configured by softwareinstructions that embody logic operable to perform function(s). Inexample embodiments where circuitry includes a combination of hardwareand software, an implementer may write source code embodying logic andthe source code can be compiled into machine readable code that can beprocessed by the general purpose processing unit. Since one skilled inthe art can appreciate that the state of the art has evolved to a pointwhere there is little difference between hardware, software, or acombination of hardware/software, the selection of hardware versussoftware to effectuate specific functions is a design choice left to animplementer. More specifically, one of skill in the art can appreciatethat a software process can be transformed into an equivalent hardwarestructure, and a hardware structure can itself be transformed into anequivalent software process. Thus, the selection of a hardwareimplementation versus a software implementation is one of design choiceand left to the implementer.

In FIG. 3B, the computing environment 220 comprises a computer 241,which typically includes a variety of computer readable media. Computerreadable media can be any available media that can be accessed bycomputer 241 and includes both volatile and nonvolatile media, removableand non-removable media. The system memory 222 includes computer storagemedia in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) 223 and random access memory (RAM) 260. A basicinput/output system 224 (BIOS), containing the basic routines that helpto transfer information between elements within computer 241, such asduring start-up, is typically stored in ROM 223. RAM 260 typicallycontains data and/or program modules that are immediately accessible toand/or presently being operated on by processing unit 259. By way ofexample, and not limitation, FIG. 3B illustrates operating system 225,application programs 226, other program modules 227, and program data228.

The computer 241 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 3B illustrates a hard disk drive 238 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 239that reads from or writes to a removable, nonvolatile magnetic disk 254,and an optical disk drive 240 that reads from or writes to a removable,nonvolatile optical disk 253 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 238 is typically connectedto the system bus 221 through an non-removable memory interface such asinterface 234, and magnetic disk drive 239 and optical disk drive 240are typically connected to the system bus 221 by a removable memoryinterface, such as interface 235.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 3B, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 241. In FIG. 3B, for example, hard disk drive 238 isillustrated as storing operating system 258, application programs 257,other program modules 256, and program data 255. Note that thesecomponents can either be the same as or different from operating system225, application programs 226, other program modules 227, and programdata 228. Operating system 258, application programs 257, other programmodules 256, and program data 255 are given different numbers here toillustrate that, at a minimum, they are different copies. A user mayenter commands and information into the computer 241 through inputdevices such as a keyboard 251 and pointing device 252, commonlyreferred to as a mouse, trackball or touch pad. Other input devices (notshown) may include a microphone, joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 259 through a user input interface 236 that iscoupled to the system bus, but may be connected by other interface andbus structures, such as a parallel port, game port or a universal serialbus (USB). The cameras 26, 28 and capture device 20 may defineadditional input devices for the console 100. A monitor 242 or othertype of display device is also connected to the system bus 221 via aninterface, such as a video interface 232. In addition to the monitor,computers may also include other peripheral output devices such asspeakers 244 and printer 243, which may be connected through a outputperipheral interface 233.

The computer 241 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer246. The remote computer 246 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 241, although only a memory storage device 247 has beenillustrated in FIG. 3B. The logical connections depicted in FIG. 3Binclude a local area network (LAN) 245 and a wide area network (WAN)249, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 241 is connectedto the LAN 245 through a network interface or adapter 237. When used ina WAN networking environment, the computer 241 typically includes amodem 250 or other means for establishing communications over the WAN249, such as the Internet. The modem 250, which may be internal orexternal, may be connected to the system bus 221 via the user inputinterface 236, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 241, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 3B illustrates remoteapplication programs 248 as residing on memory device 247. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

FIG. 4A depicts an example skeletal mapping of a user that may begenerated from the capture device 20. In this embodiment, a variety ofjoints and bones are identified: hand 302, forearm 304, elbow 306, bicep308, shoulder 310, hip 312, thigh 314, knee 316, foreleg 318, foot 320,the head 322, the torso 324, the top 326 and bottom 328 of the spine,and the waist 330. Where more points are tracked, additional featuresmay be identified, such as the bones and joints of the fingers or toes,or individual features of the face, such as the nose and eyes.

Through moving his body, a user may create gestures. A gesture comprisesa motion or pose by a user that may be captured as image data and parsedfor meaning. A gesture may be dynamic, comprising a motion, such asmimicking throwing a ball. A gesture may be static, such as holdingone's crossed forearms 304 in front of his torso 324. A gesture may alsoincorporate props, such as by swinging a mock sword. A gesture maycomprise more than one body part, such as clapping the hands 302together, or a subtler motion, such as pursing one's lips.

Gestures may be used for input in a general computing context. Forinstance, various motions of the hands 302 or other body parts maycorrespond to common system wide tasks such as to navigate up or down ina hierarchical list, open a file, close a file, and save a file.Gestures may also be used in a video-game-specific context, depending onthe game. For instance, with a driving game, various motions of thehands 302 and feet 320 may correspond to steering a vehicle in adirection, shifting gears, accelerating, and breaking.

A user may generate a gesture that corresponds to walking or running, bywalking or running in place himself. The user may alternately lift anddrop each leg 312-320 to mimic walking, without moving through a room orplace. The system may parse this gesture by analyzing each hip 312 andeach thigh 314. A step may be recognized when one hip-thigh angle (asmeasured relative to a vertical line, wherein a standing leg has ahip-thigh angle of 0°, and a forward horizontally extended leg has ahip-thigh angle of 90°) exceeds a certain threshold relative to theother thigh. A walk or run may be recognized after some number ofconsecutive steps by alternating legs. The time between the two mostrecent steps may be thought of as a period. After some number of periodswhere that threshold angle is not met, the system may determine that thewalk or running gesture has ceased.

Given a “walk or run” gesture, an application may set values forparameters associated with this gesture. These parameters may includethe above threshold angle, the number of steps required to initiate awalk or run gesture, a number of periods where no step occurs to end thegesture, and a threshold period that determines whether the gesture is awalk or a run. A fast period may correspond to a run, as the user willbe moving his legs quickly, and a slower period may correspond to awalk.

A gesture may be associated with a set of default parameters at firstthat the application may override with its own parameters. In thisscenario, an application is not forced to provide parameters, but mayinstead use a set of default parameters that allow the gesture to berecognized in the absence of application-defined parameters.

There are a variety of outputs that may be associated with the gesture.There may be a baseline “yes or no” as to whether a gesture isoccurring. There also may be a confidence level, which corresponds tothe likelihood that the user's tracked movement corresponds to thegesture. This could be a linear scale that ranges over floating pointnumbers between 0 and 1, inclusive. Wherein an application receivingthis gesture information cannot accept false-positives as input, it mayuse only those recognized gestures that have a high confidence level,such as at least 0.95. Where an application must recognize everyinstance of the gesture, even at the cost of false-positives, it may usegestures that have at least a much lower confidence level, such as thosemerely greater than 0.2. The gesture may have an output for the timebetween the two most recent steps, and where only a first step has beenregistered, this may be set to a reserved value, such as −1 (since thetime between any two steps must be positive). The gesture may also havean output for the highest thigh angle reached during the most recentstep.

Another exemplary gesture is a “heel lift jump.” In this, a user maycreate the gesture by raising his heels off the ground, but keeping histoes planted on the ground. Alternatively, the user may jump into theair where his feet 320 leave the ground entirely. The system may parsethe skeleton for this gesture by analyzing the angle of relation of theshoulders 310, hips 312 and knees 316 to see if they are in a positionof alignment equal to standing up straight. Then these points and upper326 and lower 328 spine points may be monitored for any upwardacceleration. A sufficient combination of acceleration may trigger ajump gesture.

Given this “heel lift jump” gesture, an application may set values forparameters associated with this gesture. The parameters may include theabove acceleration threshold, which determines how fast some combinationof the user's shoulders 310, hips 312 and knees 316 must move upward totrigger the gesture, as well as a maximum angle of alignment between theshoulders 310, hips 312 and knees 316 at which a jump may still betriggered.

The outputs may comprise a confidence level, as well as the user's bodyangle at the time of the jump.

Setting parameters for a gesture based on the particulars of theapplication that will receive the gesture is important in accuratelyidentifying gestures. Properly identifying gestures and the intent of auser greatly helps in creating a positive user experience. Where agesture recognizer system is too sensitive, and even a slight forwardmotion of the hand 302 is interpreted as a throw, the user may becomefrustrated because gestures are being recognized where he has no intentto make a gesture, and thus, he lacks control over the system. Where agesture recognizer system is not sensitive enough, the system may notrecognize conscious attempts by the user to make a throwing gesture,frustrating him in a similar manner. At either end of the sensitivityspectrum, the user becomes frustrated because he cannot properly provideinput to the system.

Another parameter to a gesture may be a distance moved. Where a user'sgestures control the actions of an avatar in a virtual environment, thatavatar may be arm's length from a ball. If the user wishes to interactwith the ball and grab it, this may require the user to extend his arm302-310 to full length while making the grab gesture. In this situation,a similar grab gesture where the user only partially extends his arm302-310 may not achieve the result of interacting with the ball.

A gesture or a portion thereof may have as a parameter a volume of spacein which it must occur. This volume of space may typically be expressedin relation to the body where a gesture comprises body movement. Forinstance, a football throwing gesture for a right-handed user may berecognized only in the volume of space no lower than the right shoulder310 a, and on the same side of the head 322 as the throwing arm 302a-310 a. It may not be necessary to define all bounds of a volume, suchas with this throwing gesture, where an outer bound away from the bodyis left undefined, and the volume extends out indefinitely, or to theedge of the scene that is being monitored.

FIG. 4B provides further details of one exemplary embodiment of thegesture recognizer engine 190 of FIG. 2. As shown, the gesturerecognizer engine 190 may comprise at least one filter 418 to determinea gesture or gestures. A filter 418 comprises information defining agesture 426 (hereinafter referred to as a “gesture”) along withparameters 428, or metadata, for that gesture. For instance, a throw,which comprises motion of one of the hands from behind the rear of thebody to past the front of the body, may be implemented as a gesture 426comprising information representing the movement of one of the hands ofthe user from behind the rear of the body to past the front of the body,as that movement would be captured by the depth camera. Parameters 428may then be set for that gesture 426. Where the gesture 426 is a throw,a parameter 428 may be a threshold velocity that the hand has to reach,a distance the hand must travel (either absolute, or relative to thesize of the user as a whole), and a confidence rating by the recognizerengine that the gesture occurred. These parameters 428 for the gesture426 may vary between applications, between contexts of a singleapplication, or within one context of one application over time.

Filters may be modular or interchangeable. In an embodiment, a filterhas a number of inputs, each of those inputs having a type, and a numberof outputs, each of those outputs having a type. In this situation, afirst filter may be replaced with a second filter that has the samenumber and types of inputs and outputs as the first filter withoutaltering any other aspect of the recognizer engine architecture. Forinstance, there may be a first filter for driving that takes as inputskeletal data and outputs a confidence that the gesture associated withthe filter is occurring and an angle of steering. Where one wishes tosubstitute this first driving filter with a second drivingfilter—perhaps because the second driving filter is more efficient andrequires fewer processing resources—one may do so by simply replacingthe first filter with the second filter so long as the second filter hasthose same inputs and outputs—one input of skeletal data type, and twooutputs of confidence type and angle type.

A filter need not have a parameter. For instance, a “user height” filterthat returns the user's height may not allow for any parameters that maybe tuned. An alternate “user height” filter may have tunableparameters—such as to whether to account for a user's footwear,hairstyle, headwear and posture in determining the user's height.

Inputs to a filter may comprise things such as joint data about a user'sjoint position, like angles formed by the bones that meet at the joint,RGB color data from the scene, and the rate of change of an aspect ofthe user. Outputs from a filter may comprise things such as theconfidence that a given gesture is being made, the speed at which amotion that is part of a gesture is made, and a time at which a motionthat is part of a gesture is made.

A context may be a cultural context, and it may be an environmentalcontext. A cultural context refers to the culture of a user using asystem. Different cultures may use similar gestures to impart markedlydifferent meanings. For instance, an American user who wishes to tellanother user to “look” or “use his eyes” may put his index finger on hishead close to the distal side of his eye. However, to an Italian user,this gesture may be interpreted as a reference to the mafia.

Similarly, there may be different contexts among different environmentsof a single application. Take a first-person shooter game that involvesoperating a motor vehicle. While the user is on foot, making a fist withthe fingers towards the ground and then extending the fist in front andaway from the body may represent a punching gesture. While the user isin the driving context, that same motion may represent a “gear shifting”gesture. There may also be one or more menu environments, where the usercan save his game, select among his character's equipment or performsimilar actions that do not comprise direct game-play. In thatenvironment, this same gesture may have a third meaning, such as toselect something or to advance to another screen.

The gesture recognizer engine 190 may have a base recognizer engine 416that provides functionality to a gesture filter 418. In an embodiment,the functionality that the recognizer engine 416 implements includes aninput-over-time archive that tracks recognized gestures and other input,a Hidden Markov Model implementation (where the modeled system isassumed to be a Markov process—one where a present state encapsulatesany past state information necessary to determine a future state, so noother past state information must be maintained for this purpose—withunknown parameters, and hidden parameters are determined from theobservable data), as well as other functionality required to solveparticular instances of gesture recognition.

Filters 418 are loaded and implemented on top of the base recognizerengine 416 and can utilize services provided by the engine 416 to allfilters 418. In an embodiment, the base recognizer engine 416 processesreceived data to determine whether it meets the requirements of anyfilter 418. Since these provided services, such as parsing the input,are provided once by the base recognizer engine 416 rather than by eachfilter 418, such a service need only be processed once in a period oftime as opposed to once per filter 418 for that period, so theprocessing required to determine gestures is reduced.

An application may use the filters 418 provided by the recognizer engine190, or it may provide its own filter 418, which plugs in to the baserecognizer engine 416. In an embodiment, all filters 418 have a commoninterface to enable this plug-in characteristic. Further, all filters418 may utilize parameters 428, so a single gesture tool as describedbelow may be used to debug and tune the entire filter system 418.

These parameters 428 may be tuned for an application or a context of anapplication by a gesture tool 420. In an embodiment, the gesture tool420 comprises a plurality of sliders 422, each slider 422 correspondingto a parameter 428, as well as a pictorial representation of a body 424.As a parameter 428 is adjusted with a corresponding slider 422, the body424 may demonstrate both actions that would be recognized as the gesturewith those parameters 428 and actions that would not be recognized asthe gesture with those parameters 428, identified as such. Thisvisualization of the parameters 428 of gestures provides an effectivemeans to both debug and fine tune a gesture.

FIG. 5 depicts more complex gestures or filters 418 created from stackedgestures or filters 418. Gestures can stack on each other. That is, morethan one gesture may be expressed by a user at a single time. Forinstance, rather than disallowing any input but a throw when a throwinggesture is made, or requiring that a user remain motionless save for thecomponents of the gesture (e.g. stand still while making a throwinggesture that involves only one arm), where gestures stack, a user maymake a jumping gesture and a throwing gesture simultaneously, and bothof these gestures will be recognized by the gesture engine.

FIG. 5A depicts a simple gesture filter 418 according to the stackingparadigm. The IFilter filter 502 is a basic filter 418 that may be usedin every gesture filter. IFilter 502 takes user position data 504 andoutputs a confidence level 506 that a gesture has occurred. It alsofeeds that position data 504 into a SteeringWheel filter 508 that takesit as an input and outputs an angle to which the user is steering (e.g.40 degrees to the right of the user's current bearing) 510.

FIG. 5B depicts a more complex gesture that stacks filters 418 onto thegesture filter of FIG. 5A. In addition to IFilter 502 and SteeringWheel508, there is an ITracking filter 512 that receives position data 504from IFilter 502 and outputs the amount of progress the user has madethrough a gesture 514. ITracking 512 also feeds position data 504 toGreaseLightning 516 and EBrake 518, which are filters 418 regardingother gestures that may be made in operating a vehicle, such as usingthe emergency brake.

FIG. 6 depicts an example gesture that a user 602 may make to signal fora “fair catch” in a football video game. These figures depict the userat points in time, with FIG. 6A being the first point in time, and FIG.6E being the last point in time. Each of these figures may correspond toa snapshot or frame of image data as captured by a depth camera 402,though not necessarily consecutive frames of image data, as the depthcamera 402 may be able to capture frames more rapidly than the user maycover the distance. For instance, this gesture may occur over a periodof 3 seconds, and where a depth camera captures data at 40 frames persecond, it would capture 60 frames of image data while the user 602 madethis fair catch gesture.

In FIG. 6A, the user 602 begins with his arms 604 down at his sides. Hethen raises them up and above his shoulders as depicted in FIG. 6B andthen further up, to the approximate level of his head, as depicted inFIG. 6C. From there, he lowers his arms 604 to shoulder level, asdepicted in FIG. 6D, and then again raises them up, to the approximatelevel of his head, as depicted in FIG. 6E. Where a system captures thesepositions by the user 602 without any intervening position that maysignal that the gesture is cancelled, or another gesture is being made,it may have the fair catch gesture filter output a high confidence levelthat the user 602 made the fair catch gesture.

FIG. 7 depicts the example “fair catch” gesture of FIG. 5 as each frameof image data has been parsed to produce a skeletal map of the user. Thesystem, having produced a skeletal map from the depth image of the user,may now determine how that user's body moves over time, and from that,parse the gesture.

In FIG. 7A, the user's shoulders 310, are above his elbows 306, which inturn are above his hands 302. The shoulders 310, elbows 306 and hands302 are then at a uniform level in FIG. 7B. The system then detects inFIG. 7C that the hands 302 are above the elbows, which are above theshoulders 310. In FIG. 7D, the user has returned to the position of FIG.7B, where the shoulders 310, elbows 306 and hands 302 are at a uniformlevel. In the final position of the gesture, shown in FIG. 7E, the userreturns to the position of FIG. 7C, where the hands 302 are above theelbows, which are above the shoulders 310.

While the capture device 20 captures a series of still images, such thatin any one image the user appears to be stationary, the user is movingin the course of performing this gesture (as opposed to a stationarygesture, as discussed supra). The system is able to take this series ofposes in each still image, and from that determine the confidence levelof the moving gesture that the user is making.

In performing the gesture, a user is unlikely to be able to create anangle as formed by his right shoulder 310 a, right elbow 306 a and righthand 302 a of, for example, between 140° and 145°. So, the applicationusing the filter 418 for the fair catch gesture 426 may tune theassociated parameters 428 to best serve the specifics of theapplication. For instance, the positions in FIGS. 7C and 7E may berecognized any time the user has his hands 302 above his shoulders 310,without regard to elbow 306 position. A set of parameters that are morestrict may require that the hands 302 be above the head 310 and that theelbows 306 be both above the shoulders 310 and between the head 322 andthe hands 302. Additionally, the parameters 428 for a fair catch gesture426 may require that the user move from the position of FIG. 7A throughthe position of FIG. 7E within a specified period of time, such as 1.5seconds, and if the user takes more than 1.5 seconds to move throughthese positions, it will not be recognized as the fair catch 418, and avery low confidence level may be output.

FIG. 8 depicts exemplary operational procedures for using a gesturerecognizer architecture to provide gestures to at least one applicationof a plurality of applications.

Operation 802 depicts providing a filter representing a gesture to thefirst application, the filter comprising base information about thegesture. The gesture may comprise a wide variety of gestures. It may,for instance, be any of a crouch, a jump, a lean, an arm throw, a toss,a swing, a dodge, a kick, and a block. Likewise, the gesture maycorrespond to navigation of a user interface. For instance, a user mayhold his hand with the fingers pointing up and the palm facing the depthcamera. He may then close his fingers towards the palm to make a fist,and this could be a gesture that indicates that the focused window in awindow-based user-interface computing environment should be closed.

As gestures may be used to indicate anything from that an avatar shouldthrow a punch to that a window should be closed, a wide variety ofapplications, from video games to text editors may utilize gestures.

Gestures may be grouped together into genre packages of complimentarygestures that are likely to be used by an application in that genre.Complimentary gestures—either complimentary as in those that arecommonly used together, or complimentary as in a change in a parameterof one will change a parameter of another—are grouped together intogenre packages. These packages are provided to an application, which mayselect at least one. The application may tune, or modify, the parameterof a gesture to best fit the unique aspects of the application. Whenthat parameter is tuned, a second, complimentary parameter (in theinterdependent sense) of either the gesture or a second gesture is alsotuned such that the parameters remain complimentary. Genre packages forvideo games may include genres such as first-person shooter, action,driving, and sports.

The parameter may vary based on the context the application is in. Tothat end, an application may assign a plurality of values to a parameterfor a gesture, each value corresponding to a different context. Asdiscussed supra, this context may be a cultural context or anenvironmental context.

In an embodiment, the application provides the gesture, which is used bythe gesture recognizer engine. In the embodiment where each gesturecomprises common inputs and outputs, the application may provide agesture that adheres to those conventions, and communicate this gesturewith the recognizer engine through an agreed-upon protocol.

Operation 804 depicts receiving data captured by capture device 20, asdescribed above, the data corresponding to the first application. Thedata may correspond to the first application because that is thecurrently active application for which input is being generated.

Operation 806 depicts applying the filter to the data and determining anoutput from the base information about the gesture. In an embodiment,the output comprises a confidence level that the gesture correspondingto the filter has been performed. This confidence level may be outputonce, or it may be output continuously in response to received data. Inan embodiment, this comprises determining a confidence level that theuser has moved in such a way as to meet any requirements of the gesture(such as the hand being above the head) that are independent of anyparameters. In an embodiment, this output may comprise a booleandetermination as to whether the gesture corresponding to the filteroccurred.

Where the filter comprises a parameter, the parameter may be athreshold, such as arm velocity is greater than X. It may be anabsolute, such as arm velocity equals X. There may be a fault tolerance,such as arm velocity equals within Y of X. It may also comprise a range,such as arm velocity is greater than or equal to X, but less than Z.From the received data, the characteristics of that data that apply tothe parameter may be determined, and then compared to the requirementsof the parameter.

In an embodiment, the user also uses his voice to make, augment,distinguish or clarify a gesture. In this embodiment, operation 806comprises receiving voice data and determining that a combination of thedata and the voice data is indicative of the gesture. For instance, auser may be able to make a singing gesture by opening and closing hismouth, but also specify a specific note of that singing gesture bysinging that note. Additionally, the user may be able to make a “strongpunch” gesture as opposed to a “regular punch” gesture by shouting whilemaking what is otherwise a “regular punch” gesture.

In an embodiment, the gesture may comprise a plurality of gestures. Forinstance, the user may be making the motions corresponding to moving tohis side and discharging his firearm simultaneously. In this embodiment,it would be disfavored to limit the user to not discharging his firearmwhile he is moving, so multiple gestures made simultaneously by the userare detected.

Operation 808 depicts sending the first application the confidencelevel. In an embodiment, this may include sending the application a timeor a period of time at which the gesture occurred. In another embodimentwhere the application desires time information, the application may usethe time at which this indication that the gesture occurred is receivedas that time information. In an embodiment, this operation includessending the application information about the characteristics of thegesture, such as a velocity of movement, a release point, a distance,and a body part that made the gesture. For instance, given a baseballvideo game where a pitcher may throw a pitch at any integer velocitybetween 50 mph and 105 mph, inclusive, and that velocity is based on theuser's maximum arm velocity in making the gesture, it may be cumbersometo define a separate set of parameters for each of those 56 possiblevelocities. Instead, the application may be given an indication that thegesture occurred along with the maximum arm velocity of the user, andthen the application may use internal logic to determine how fast thecorresponding pitcher should throw the pitch.

Optional operation 810 depicts receiving from the first application avalue for at least one parameter, and where determining from the baseinformation about the gesture and each parameter a confidence levelincludes determining from the value of the parameter a confidence level.A parameter may comprise any of a wide variety of characteristics of agesture, such as a body part, a volume of space, a velocity, a directionof movement, an angle, and a place where a movement occurs.

In an embodiment, the value of the parameter is determined by an enduser of the application through making a gesture. For instance, anapplication may allow the user to train it, so that the user is able tospecify what motions he believes a gesture should comprise. This may bebeneficial to allow a user without good control over his motor skills tobe able to link what motions he can make with a corresponding gesture.If this were not available, the user may become frustrated because he isunable to make his body move in the manner as the application requiresthat the gesture be produced.

In an embodiment where there exist complimentary gestures—a plurality ofgestures that have inter-related parameters—receiving from theapplication a value for a parameter may include both setting theparameter with the value, and setting a complimentary parameter of acomplimentary gesture based on the value. For example, one may decidethat a user who throws a football in a certain manner is likely to alsothrow a baseball in a certain manner. So, where it is determined that acertain parameter should be set in a particular manner, othercomplimentary parameters may be set based on how that first parameter isset.

This need not be the same value for a given parameter, or even the sametype of parameter across gestures. For instance, it could be that when afootball throw must be made with a forward arm velocity of X m/s, then afootball catch must be made with the hands at least distance Y m awayfrom the torso.

Operation 812 depicts the optional operation of receiving from thesecond application a second value for at least one parameter of a secondfilter representing the gesture, the second filter comprising the baseinformation about the gesture, the second value differing from the valuereceived from the first application; receiving second data captured by acamera; applying the second filter to the second data and determiningfrom the base information about the gesture and each parameter of thesecond filter a confidence level that the second data is indicative ofthe gesture being performed; and sending the second application theconfidence level.

Each application, or context within an application, may specify its ownparameter for a single gesture, and the gesture recognizer 190 will beresponsive to the particulars of each application. For instance, onefirst-person shooter may require a demonstrative gun firing gesture,because ammunition is limited or secrecy plays a major role in the game,and firing a gun produces a loud noise. However, a second first-personshooter may allow for a much smaller motion for firing a gun, because ademonstrative motion runs too close to what that game has defined for apunching motion.

Optional operation 814 depicts optional operations—wherein receivingfrom the application a value for a parameter includes setting theparameter with the value, and receiving data captured by the capturedevice includes receiving data from a first user—of setting theparameter with a second value in response to receiving data captured bythe capture device indicative of one selected from the group consistingof an increase in the first user's fatigue, an increase in the firstuser's competence, and a second user replacing the first user.

The motions or poses that a user makes to convey a gesture may changeover time, and this may create a change in context. For instance, theuser may become fatigued and no longer jump as high as he did at thestart of his session to convey a jump gesture. Also, the user maythrough practice become better at making the motion or pose associatedwith a gesture, so the acceptable variations for a parameter may bedecreased so that two different gestures are less likely to berecognized by one set of motions or pose. The application may also wishto give the user a more challenging experience as he becomes moreadroit, so as not to lose his attention. In another embodiment, theapplication may give the user finer control over the gestures or moregestures to use. For instance, a beginner at a tennis video game mayonly be concerned with hitting the ball over the net, and not withputting any spin on it. However, as the tennis application detects thatthe user is improving, it may introduce or recognize gestures fortop-spin or back-spin in addition to the standard hit.

Also, a second user may replace the first user as the one providinginput and the second user may have drastically different ways ofconveying gestures. In an embodiment, the received depth images may beparsed to determine that a second user is in the scene conveying thegestures. In an embodiment, there may be a reserved gesture that theuser may utilize to indicate this to the application. Also, therecognizer engine may determine the presence of the second user througha change in the motions or poses made to convey gestures.

When these changes have been determined, they may each be treated like achange in context, and parameters may be changed correspondingly toaccount for this.

Operation 816 depicts the optional operation of changing the context;receiving a second data captured by the camera indicative of the user'sfirst gesture being performed; and determining from the second data thatit is more likely that a second gesture represented by a second filteris being performed than it is that the first gesture represented by thefirst filter is being performed.

In different contexts, one motion or pose by a user may be correspond todifferent gestures. For instance, in a fighting game, while in thefighting mode of the game, the user may cross his forearms in front ofhis torso. In this fighting context, that may correspond to a “blockattack” gesture. However, if the user pauses the game and enters a menuscreen, this menu screen comprises a different context. In this menuscreen context, that same crossed forearms may correspond to a “exitmenu screen” gesture.

FIGS. 9A and 9B depict predictive detection of user input. Where a useris utilizing gesture input in a scenario where low latency is importantto the quality of user experience, such as a car racing game, it may bethat waiting until the user has completed a gesture takes too long. Notonly is there the latency in identifying the gesture by the gesturerecognizer engine, but there is also, for instance, latency (1) betweenwhen the user starts the gesture and that starting of the gesture by theuser is captured by a depth camera, (2) between when the depth cameracaptures the data and the gesture recognizer engine receives it, (3)between when the gesture recognizer engine outputs an indication of thegesture to an application and that application receives it, and (4)between the time the application receives it and it processes anddisplays it on a display device for the user to see.

To that end, to aid in reducing latency, predictive detection ofgestures may be implemented. Using such predictive detection, an outputfrom a gesture filter corresponding to the gesture being detected may beindicated at some point before the gesture has been fully made, butafter enough data has been received that the indication may be made withsome certainty.

For example, given a baseball pitch gesture filter, the recognizerengine may predict that the gesture will occur as soon as the handholding the ball moves past the head of the user. Once that hand movespast the head, given the acceleration and velocity (and thereforemomentum) of the arm, the most likely scenario is that the arm willcontinue to perform the pitch gesture. A filter may be implemented suchthat it considers a gesture complete before it is actually fullyindicated by the user.

Gestures may be predicted by using past data to predict future changesin the data tracked by the gesture recognizer. Such data may includebody part position, velocity, acceleration or rotation, or any otherdata received from a capture device, such as movement of a physicalobject, an input to a mouse or controller, or audio signals. In anembodiment, this past data comprises data where the user made thecompleted gesture. Where at some point in making that gesture, the userhad a given velocity, acceleration or rotation of one or more bodyparts, and the user has a similar velocity, acceleration or rotation ofthat body part or body parts here as measured by the current data, thegesture recognizer engine may determine that it is highly probable thatthe user is performing that same gesture again, and output acorresponding indication for the filter corresponding to that gesture.

In an embodiment, the gesture recognizer engine may use a position,velocity, acceleration or rotation of one or more body parts of the userin making the current, as-yet-undetermined gesture to predict whichgesture the user is making. For example, if the user is accelerating histhrowing hand toward the front of his body, but has not yet made thethrowing gesture, the gesture recognizer engine may predict that thisacceleration of the throwing hand will continue until the user has madethe throwing gesture, and therefore output a corresponding indicationfor the throwing gesture filter.

Gestures may also be predicted by using knowledge of human anatomy topredict future body part position, velocity, acceleration or rotation.For instance, a forearm is limited in the degree that it may rotate, andmost people cannot create an angle in excess of 180 degrees in theirelbow. Additionally, where the right hand starts moving forward at arapid rate, it is likely that the right elbow and shoulder will soonbegin moving forward as well, since those body parts are all closelyphysically connected. In an embodiment, the gesture recognizer engine isaware of such human physiology and makes its predictive determinationbased on that.

In an embodiment, the output comprises an indication that the filter isbeing predicted, as opposed to has been observed. An application may usethis indication to determine how to process the rest of the output. Forinstance, an application may decide that predicted gestures areunacceptable in certain scenarios because the negative impact of awrongly predicted gesture on the user experience is too great, so inthose scenarios, it discards any outputs of the gesture recognizerengine marked as predictive.

In an embodiment, where the gesture recognizer engine receivesadditional data that corresponds to the gesture being performed, therecognizer engine outputs an indication that the gesture has beenperformed. In the above scenario, the gesture recognizer engine maychange the “predicted” indication to “performed” when the user hascompleted the gesture, and the application may then choose to processthe output of the gesture recognizer at that point.

In an embodiment, where the gesture recognizer engine receivesadditional data that corresponds to the gesture ultimately not beingperformed, the recognizer engine outputs an indication that the priorprediction was incorrect and the gesture was not performed.

In an embodiment where a gesture filter comprises a plurality ofparameters, a first parameter may be associated with an early part ofthe gesture while a second parameter may be associated with a later partof the gesture. For instance, in a baseball pitch gesture, the filtermay use parameters defining a threshold arm velocity and a releasepoint. The threshold arm velocity, being the velocity of the arm whileit holds the imaginary ball, will occur before the point at which thatball is released in the throw. Where the gesture recognizer engine ispredicting the throw, it may decide that the release point occurs toolate in the gesture to be useful in predicting the gesture, and relyonly upon the threshold arm velocity parameter in making the prediction.

In an embodiment, gestures may be linked. For instance, in a car racinggame, the user may make a clutch-depress gesture, then a gear-shiftgesture, and then a clutch-release gesture. In this scenario, it may becommon that a clutch-depress gesture is followed by some form of agear-shift gesture, be it an up-shift gesture, a down-shift gesture, ora reverse-shift gesture. Where a user then makes a clutch-depressgesture, the gesture recognizer may then increase the likelihood thatthe next gesture the user makes will be a gear-shift gesture, and adjustthe output of gear-shift gesture filters accordingly.

Where two gestures appear to be the same early in the gesture, such as abaseball-fastball gesture and a fake-curveball-throw gesture, therecognizer engine may predict the gesture that will be made based on thecontext in which the user is operating. For instance, if the user iscontrolling an avatar that is a pitcher in a baseball game, and thecount is 3-0, most users at that point may wish to throw a fastballrather than a curveball, because they have a greater likelihood ofthrowing a strike with a fastball, and throwing one more ball willresult in walking the batter. Where the initial part of both gesturesare identical, and differ only in that the user rotates the hand at theend of the throw for a curveball, the gesture recognizer engine mayanalyze the context of the play to determine that a fastball issignificantly more likely to occur, and thus output a high confidencelevel for the fastball gesture filter.

In an embodiment, the recognizer engine will predict that each of thesegestures is observed and allow an application that receives the data todetermine how to process that information. In an embodiment, therecognizer engine will wait to predict that a gesture occurs until thereis no ambiguity as to which gesture is being performed.

FIG. 9A depicts a graph charting the confidence level 902 a output by athrowing gesture filter over time as a function of the amplitude of athrowing hand position 904 a. The maximum amplitude is reached shortlyafter a time of four. At this point, the recognizer engine is able tosay with high certainty that a throw gesture is being performed and mayoutput a high confidence level for that gesture filter. The recognizerengine requires time to make such a determination, so it outputs itspeak confidence level for the throwing filter at a time of six. Thisdelay between when the user starts the gesture until when the gesturerecognizer engine outputs a high confidence level for the correspondingfilter represents a latency that may be reduced by the presentpredictive determination.

FIG. 9B depicts a graph charting the confidence level 902 b output by athrowing gesture filter over time as a function of the amplitude of athrowing hand position 904 b using predictive determination. Whereas inFIG. 6A, the maximum confidence 902 a is output after the maximum handposition amplitude 904 a is reached, here this confidence 902 b isoutput before the maximum hand position amplitude 904 b is reached,through prediction of what the user will do. In an embodiment, thegesture recognizer engine may take data of a monotonically increasinghand amplitude and from that determine with sufficient certainty thatthe hand amplitude will be such in the future that it immediatelyoutputs a high confidence level for the throwing filter. In the presentfigures, the confidence level 902 b of FIG. 9B corresponds to theconfidence level 902 a of FIG. 9A, but on a shorter time scale. Thegeneral shape of the confidence level 902 b graph remains the same, withtwo peaks, but the second peak centers around a time of four rather thana time of six. That is, where the gesture filter continuously outputs avalue for a variable, such as a confidence level, the output 902 b of apredicted gesture filter, like in FIG. 9B may correspond to the output902 a of an unpredicted gesture filter, like in FIG. 9A.

CONCLUSION

While the present disclosure has been described in connection with thepreferred aspects, as illustrated in the various figures, it isunderstood that other similar aspects may be used or modifications andadditions may be made to the described aspects for performing the samefunction of the present disclosure without deviating there from.Therefore, the present disclosure should not be limited to any singleaspect, but rather construed in breadth and scope in accordance with theappended claims. For example, the various procedures described hereinmay be implemented with hardware or software, or a combination of both.Thus, the methods and apparatus of the disclosed embodiments, or certainaspects or portions thereof, may take the form of program code (i.e.,instructions) embodied in tangible media, such as floppy diskettes,CD-ROMs, hard drives, or any other machine-readable storage medium. Whenthe program code is loaded into and executed by a machine, such as acomputer, the machine becomes an apparatus configured for practicing thedisclosed embodiments. In addition to the specific implementationsexplicitly set forth herein, other aspects and implementations will beapparent to those skilled in the art from consideration of thespecification disclosed herein. It is intended that the specificationand illustrated implementations be considered as examples only.

1. A method for predicting a gesture made by a user to a firstapplication, comprising: receiving image data captured by a camera andsound data captured by a microphone, wherein the image data isrepresentative of a gesture performed by the user and the sound data isrepresentative of a sound made by the user; applying a filter to theimage data to interpret the gesture, wherein the sound data at least oneof: augments, distinguishes or clarifies the gesture and wherein thefilter comprises a first parameter about the gesture and a secondparameter about the gesture, the first parameter corresponding to anearlier part of the gesture than the second parameter; determining, fromthe applied filter, an output corresponding to the gesture beingperformed, wherein determining the output includes determining theoutput corresponds to a high confidence level when the first parametercorresponds to a high confidence level and the second parameter does notcorrespond to a high confidence level; and sending the first applicationthe output.
 2. The method of claim 1, wherein determining, from theapplied filter, an output corresponding to the gesture being performedincludes: determining that future received data will correspond to thegesture being performed based on a correlation between the image andsound data and a prior data that corresponds to the gesture beingcompleted.
 3. The method of claim 1, wherein determining, from theapplied filter, an output corresponding to the gesture being performedincludes: determining that future received data will correspond to thegesture being performed based on a physiology of the user.
 4. The methodof claim 1, wherein the output comprises a confidence level that thedata is indicative of the gesture being performed.
 5. The method ofclaim 1, wherein the output comprises an indication that the filter isbeing predicted.
 6. The method of claim 1, further comprising: receivingsecond sound and second image data; applying the filter to the secondimage data to interpret the gesture, wherein the second sound data atleast one of: augments, distinguishes or clarifies the gesture;determining from the applied filter that the gesture has been performed;determining a second output based on the applied filter; and sending thefirst application the second output.
 7. The method of claim 6, whereinthe output comprises an indication that the filter is being predictedand the second output comprises an indication that the filter has beencompleted.
 8. The method of claim 1, further comprising: receivingsecond sound and second image data; applying the filter to the secondimage data to interpret the gesture, wherein the second sound data atleast one of: augments, distinguishes or clarifies the gesture;determining from the applied filter that the gesture was not performed;determining a second output based on the applied filter, the secondoutput indicating that the gesture was not performed; and sending thefirst application the second output.
 9. The method of claim 1, furthercomprising: receiving a parameter from the first application, theparameter defining a threshold of one of a volume of space, a velocity,a direction of movement, an angle, or a place where a movement occurs;and wherein applying a filter to the image data to interpret the gestureincludes applying the parameter to the image data to interpret thegesture.
 10. The method of claim 1, wherein the output corresponds to anoutput that would be determined when the gesture was completed.
 11. Themethod of claim 1, wherein the filter is linked to a previous filter,and determining, from the applied filter, an output corresponding to thegesture being performed, includes: determining the output based on aprior output of the previous filter.
 12. The method of claim 1, furthercomprising: applying a second filter to the image data to interpret thegesture, the second filter representing a second gesture and comprisingbase information about the second gesture; determining, from the appliedsecond filter, a second output corresponding to the second gesture beingperformed and a context, the second output being indicative of a greaterconfidence level than the output; and sending the first application thesecond output.
 13. A system for predicting a gesture made by a user to afirst application, comprising: a processor; a gesture library comprisingat least one filter to interpret the gesture; and a gesture recognizerengine that: receives image data captured by a camera and sound datacaptured by a microphone, wherein the image data is representative of agesture performed by the user and the sound data is representative of asound made by the user; determines an output from the filter based onthe image data, wherein the sound data at least one of: augments,distinguishes or clarifies the gesture; sends the application the outputbefore receiving data corresponding to the gesture being completed;applies a second filter to the image data to interpret the gesture, thesecond filter representing a second gesture and comprising baseinformation about the second gesture; determines, from the appliedsecond filter, a second output corresponding to the second gesture beingperformed and a context, the second output being indicative of a greaterconfidence level than the output; and sends the application the secondoutput.
 14. The system of claim 13, wherein the gesture recognizerengine further: sends the application at least one selected from thegroup consisting of a velocity of movement, a release point, a distance,and a body part that made the gesture.
 15. The system of claim 13,wherein the gesture recognizer engine further: sends the application aplurality of outputs, each output corresponding to a filter of aplurality of filters.
 16. A computer readable storage medium, comprisingcomputer readable instructions that when executed on a processor, causethe processor to perform the operations of: receiving from a firstapplication of the a plurality of applications a value for at least oneparameter; receiving image data captured by a depth camera and sounddata captured by a microphone, wherein the image data is representativeof a gesture performed by the user and the sound data is representativeof a sound made by the user; applying the filter to the image data tointerpret the gesture, wherein the sound data at least one of: augments,distinguishes or clarifies the gesture, and wherein the filter comprisesa first parameter and a second parameter about the gesture, the firstparameter corresponding to an earlier part of the gesture than thesecond parameter; determining a confidence level that the image andsound data is indicative of the at least one gesture, whereindetermining the confidence level includes determining the confidencelevel corresponds to a high confidence level when the first parametercorresponds to a high confidence level and the second parameter does notcorrespond to a high confidence level; and sending the first applicationan indication of at least one gesture with its associated confidencelevel.
 17. The computer-readable storage medium of claim 16, furthercomprising: applying a second filter to the image data to interpret thegesture; determining, from the applied second filter, a confidence levelthat the image and sound data is indicative of at least one gesture, thesecond confidence level being greater than the confidence level; andsending the first application the second confidence level.
 18. Thecomputer-readable storage medium of claim 16, wherein determining aconfidence level that the image and sound data is indicative of at leastone gesture includes: determining that future received data will beindicative of at least one gesture based on a correlation between theimage and sound data and prior image and sound data that is indicativeof the gesture.
 19. The computer-readable storage medium of claim 16,wherein determining a high confidence level that the image and sounddata is indicative of the at least one gesture based on the firstparameter corresponding to a high confidence level and the secondparameter not corresponding to a high confidence level comprises:determining that future received data will correspond to the gesturebeing performed based on a correlation between the image and sound dataand a prior data that corresponds to the gesture being completed. 20.The computer-readable storage medium of claim 16, wherein determining ahigh confidence level that the image and sound data is indicative of theat least one gesture based on the first parameter corresponding to ahigh confidence level and the second parameter not corresponding to ahigh confidence level comprises: determining that future received datawill correspond to the gesture being performed based on a physiology ofthe user.